DSPF: A Digital Signal Processing Based Framework for Information Retrieval

Recently, researchers mainly focus on three categories of models in the field of Information Retrieval (IR), namely vector-space models, probabilistic models, and statistical language models. The existing studies have always developed IR models through refining or combining these traditional models. However, some new frameworks (e.g., digital signal processing (DSP)-based IR framework) have not been well-developed. In our research, we propose a new DSP-based IR Framework (DSPF) introducing the theories from the field of the DSP and present two corresponding DSP-based IR models, denoted as DSPF-BM25 and DSPF-DLM, which incorporate the term weighting schemes from two well-performed probabilistic IR models, the BM25, and the Dirichlet Language Model (DLM). In particular, first, we consider each query term as a spectrum with Gaussian form. Second, instead of transforming the signals from the time domain to frequency domain, we directly represent the query terms in the frequency domain. It is much more controllable and precise to adjust the values of the parameters for getting better performance of our proposed models. To testify the effectiveness of our proposed models, we conduct extensive experiments on seven standard datasets. The results show that in most cases our proposed models outperform the strong baselines in terms of MAP.